A1276
Title: Robust estimation for dynamic spatial autoregression models with nearly optimal rates
Authors: Xuening Zhu - Fudan University (China) [presenting]
Abstract: Spatial autoregression has been extensively studied in various applications, yet its robust estimation methods have received limited attention. We introduce two dynamic spatial autoregressive (DSAR) models designed to capture temporal trends and depict the asymmetric network effects among the units. For both DSAR models, we propose a truncated Yule-Walker estimation method tailored to achieve robust estimation in the presence of heavy-tailed data. Additionally, we extend this robust estimation procedure to a constrained estimation framework using the Dantzig selector, enabling the identification of sparse network effects observed in real-world applications. Theoretically, the minimax optimality of the proposed estimators is derived under certain conditions on the weighting matrix. Empirical studies, including an analysis of financial contagion in the Chinese stock market and the dynamics of live streaming popularity, demonstrate the practical efficacy of our methods.
